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climate_data <- read.csv("/home/CAMPUS/cdma2019/ChristinaMarsh_LosAngeles_data.csv")
head(climate_data)
## STATION NAME DATE PRCP TAVG TMAX TMIN
## 1 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-01 NA NA 16.1 8.9
## 2 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-02 NA NA 18.3 6.7
## 3 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-03 NA NA 18.9 8.3
## 4 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-04 7.6 NA 17.8 11.1
## 5 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-05 15.2 NA 18.9 10.0
## 6 USW00093134 LOS ANGELES DOWNTOWN USC, CA US 1906-04-06 NA NA 15.6 10.0
str(climate_data)
## 'data.frame': 39152 obs. of 7 variables:
## $ STATION: Factor w/ 1 level "USW00093134": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME : Factor w/ 1 level "LOS ANGELES DOWNTOWN USC, CA US": 1 1 1 1 1 1 1 1 1 1 ...
## $ DATE : Factor w/ 39152 levels "1906-04-01","1906-04-02",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ PRCP : num NA NA NA 7.6 15.2 NA NA NA NA NA ...
## $ TAVG : num NA NA NA NA NA NA NA NA NA NA ...
## $ TMAX : num 16.1 18.3 18.9 17.8 18.9 15.6 16.7 19.4 17.8 16.7 ...
## $ TMIN : num 8.9 6.7 8.3 11.1 10 10 12.2 13.3 12.8 12.8 ...
names(climate_data)
## [1] "STATION" "NAME" "DATE" "PRCP" "TAVG" "TMAX" "TMIN"
plot(TMAX~DATE, climate_data)
min(climate_data$TMAX)
## [1] NA
strDates <- as.character(climate_data$DATE)
climate_data$NewDate <- as.Date(strDates, "%Y-%m-%d")
plot(TMAX~NewDate, climate_data[1:1835,], ty='l')
TMAX.lm = lm(TMAX ~ NewDate, data=climate_data)
plot(TMAX ~ NewDate, data= climate_data, las=1)
plot(TMAX~NewDate, climate_data[1:1835,], ty='l')
lm(TMAX ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMAX ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 2.362e+01 5.643e-05
plot(TMAX ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMAXMean = aggregate(TMAX ~ Month + Year, climate_data, mean)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 3 variables:
## $ Month: chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
MonthlyTMAXMean$Year.num = as.numeric(MonthlyTMAXMean$Year)
MonthlyTMAXMean$Month.num = as.numeric(MonthlyTMAXMean$Month)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 5 variables:
## $ Month : chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
## $ Year.num : num 1906 1906 1906 1906 1906 ...
## $ Month.num: num 4 5 6 7 8 9 10 11 12 1 ...
plot(MonthlyTMAXMean$TMAX, ty='l')
plot(TMAX~NewDate, climate_data[1:1835,], ty='l')
lm(TMAX ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMAX ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 2.362e+01 5.643e-05
plot(TMAX ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMAXMean = aggregate(TMAX ~ Month + Year, climate_data, mean)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 3 variables:
## $ Month: chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
MonthlyTMAXMean$Year.num = as.numeric(MonthlyTMAXMean$Year)
MonthlyTMAXMean$Month.num = as.numeric(MonthlyTMAXMean$Month)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 5 variables:
## $ Month : chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
## $ Year.num : num 1906 1906 1906 1906 1906 ...
## $ Month.num: num 4 5 6 7 8 9 10 11 12 1 ...
plot(MonthlyTMAXMean$TMAX, ty='l')
plot(TMAX~Year.num, data=MonthlyTMAXMean[MonthlyTMAXMean$Month=="08",], ty='l', xlim=c(1906, 2014))
August.lm <- lm(TMAX~Year.num, data=MonthlyTMAXMean[MonthlyTMAXMean$Month=="08",])
summary(August.lm)
##
## Call:
## lm(formula = TMAX ~ Year.num, data = MonthlyTMAXMean[MonthlyTMAXMean$Month ==
## "08", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4545 -1.0434 -0.1541 0.9059 3.4437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -16.314846 8.024619 -2.033 0.0445 *
## Year.num 0.022823 0.004081 5.592 1.76e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 106 degrees of freedom
## Multiple R-squared: 0.2278, Adjusted R-squared: 0.2205
## F-statistic: 31.27 on 1 and 106 DF, p-value: 1.762e-07
abline(coef(August.lm), col="red")
plot(TMAX~NewDate, climate_data[1:1835,], ty='l')
lm(TMAX ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMAX ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 2.362e+01 5.643e-05
plot(TMAX ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMAXMean = aggregate(TMAX ~ Month + Year, climate_data, mean)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 3 variables:
## $ Month: chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
MonthlyTMAXMean$Year.num = as.numeric(MonthlyTMAXMean$Year)
MonthlyTMAXMean$Month.num = as.numeric(MonthlyTMAXMean$Month)
str(MonthlyTMAXMean)
## 'data.frame': 1279 obs. of 5 variables:
## $ Month : chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMAX : num 20.4 20.4 26.3 28.6 29.3 ...
## $ Year.num : num 1906 1906 1906 1906 1906 ...
## $ Month.num: num 4 5 6 7 8 9 10 11 12 1 ...
plot(MonthlyTMAXMean$TMAX, ty='l')
plot(TMAX~Year.num, data=MonthlyTMAXMean[MonthlyTMAXMean$Month=="09",], ty='l', xlim=c(1906, 2014))
September.lm <- lm(TMAX~Year.num, data=MonthlyTMAXMean[MonthlyTMAXMean$Month=="09",])
summary(September.lm)
##
## Call:
## lm(formula = TMAX ~ Year.num, data = MonthlyTMAXMean[MonthlyTMAXMean$Month ==
## "09", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4906 -1.1184 -0.1358 1.3103 4.1107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.980408 10.389510 -1.538 0.127
## Year.num 0.022327 0.005281 4.228 5.09e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.703 on 104 degrees of freedom
## Multiple R-squared: 0.1467, Adjusted R-squared: 0.1385
## F-statistic: 17.87 on 1 and 104 DF, p-value: 5.086e-05
abline(coef(September.lm), col="red")
plot(TMIN~NewDate, climate_data[1:1835,], ty='l')
lm(TMIN ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMIN ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 1.345e+01 5.375e-05
plot(TMIN ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMINMean = aggregate(TMIN ~ Month + Year, climate_data, mean)
MonthlyTMINMean$Year.num = as.numeric(MonthlyTMINMean$Year)
MonthlyTMINMean$Month.num = as.numeric(MonthlyTMINMean$Month)
head(MonthlyTMINMean)
## Month Year TMIN Year.num Month.num
## 1 04 1906 13.09333 1906 4
## 2 05 1906 14.12258 1906 5
## 3 06 1906 18.07000 1906 6
## 4 07 1906 21.21290 1906 7
## 5 08 1906 19.69032 1906 8
## 6 09 1906 17.87333 1906 9
plot(MonthlyTMINMean$TMIN, ty='l')
plot(TMIN~NewDate, climate_data[1:1835,], ty='l')
lm(TMIN ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMIN ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 1.345e+01 5.375e-05
plot(TMIN ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMINMean = aggregate(TMIN ~ Month + Year, climate_data, mean)
str(MonthlyTMINMean)
## 'data.frame': 1279 obs. of 3 variables:
## $ Month: chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMIN : num 13.1 14.1 18.1 21.2 19.7 ...
MonthlyTMINMean$Year.num = as.numeric(MonthlyTMINMean$Year)
MonthlyTMINMean$Month.num = as.numeric(MonthlyTMINMean$Month)
str(MonthlyTMINMean)
## 'data.frame': 1279 obs. of 5 variables:
## $ Month : chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMIN : num 13.1 14.1 18.1 21.2 19.7 ...
## $ Year.num : num 1906 1906 1906 1906 1906 ...
## $ Month.num: num 4 5 6 7 8 9 10 11 12 1 ...
plot(MonthlyTMINMean$TMIN, ty='l')
plot(TMIN~Year.num, data=MonthlyTMINMean[MonthlyTMINMean$Month=="09",], ty='l', xlim=c(1906, 2014))
September.lm <- lm(TMIN~Year.num, data=MonthlyTMINMean[MonthlyTMINMean$Month=="09",])
summary(September.lm)
##
## Call:
## lm(formula = TMIN ~ Year.num, data = MonthlyTMINMean[MonthlyTMINMean$Month ==
## "09", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9099 -0.8724 -0.0956 0.7886 4.5072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -43.634750 8.280146 -5.270 7.44e-07 ***
## Year.num 0.030972 0.004209 7.359 4.48e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 104 degrees of freedom
## Multiple R-squared: 0.3424, Adjusted R-squared: 0.3361
## F-statistic: 54.15 on 1 and 104 DF, p-value: 4.483e-11
abline(coef(September.lm), col="red")
plot(TMIN~NewDate, climate_data[1:1835,], ty='l')
lm(TMIN ~ NewDate, data=climate_data)
##
## Call:
## lm(formula = TMIN ~ NewDate, data = climate_data)
##
## Coefficients:
## (Intercept) NewDate
## 1.345e+01 5.375e-05
plot(TMIN ~ NewDate, data= climate_data, las=1)
climate_data$Month = format(as.Date(climate_data$NewDate), format = "%m")
climate_data$Year = format(climate_data$NewDate, format="%Y")
MonthlyTMINMean = aggregate(TMIN ~ Month + Year, climate_data, mean)
str(MonthlyTMINMean)
## 'data.frame': 1279 obs. of 3 variables:
## $ Month: chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMIN : num 13.1 14.1 18.1 21.2 19.7 ...
MonthlyTMINMean$Year.num = as.numeric(MonthlyTMINMean$Year)
MonthlyTMINMean$Month.num = as.numeric(MonthlyTMINMean$Month)
str(MonthlyTMINMean)
## 'data.frame': 1279 obs. of 5 variables:
## $ Month : chr "04" "05" "06" "07" ...
## $ Year : chr "1906" "1906" "1906" "1906" ...
## $ TMIN : num 13.1 14.1 18.1 21.2 19.7 ...
## $ Year.num : num 1906 1906 1906 1906 1906 ...
## $ Month.num: num 4 5 6 7 8 9 10 11 12 1 ...
plot(MonthlyTMINMean$TMIN, ty='l')
plot(TMIN~Year.num, data=MonthlyTMINMean[MonthlyTMINMean$Month=="08",], ty='l', xlim=c(1906, 2014))
August.lm <- lm(TMIN~Year.num, data=MonthlyTMINMean[MonthlyTMINMean$Month=="08",])
summary(August.lm)
##
## Call:
## lm(formula = TMIN ~ Year.num, data = MonthlyTMINMean[MonthlyTMINMean$Month ==
## "08", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3968 -0.9869 -0.2585 0.8026 3.5369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -38.814426 7.708758 -5.035 1.97e-06 ***
## Year.num 0.028839 0.003921 7.356 4.20e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 106 degrees of freedom
## Multiple R-squared: 0.338, Adjusted R-squared: 0.3317
## F-statistic: 54.11 on 1 and 106 DF, p-value: 4.201e-11
abline(coef(August.lm), col="red")
MonthlyTMINMean = aggregate(TMIN ~ Month + Year, climate_data, mean)
MonthlyTMINMean$Year.num = as.numeric(MonthlyTMINMean$Year)
MonthlyTMINMean$Month.num = as.numeric(MonthlyTMINMean$Month)
head(MonthlyTMINMean)
## Month Year TMIN Year.num Month.num
## 1 04 1906 13.09333 1906 4
## 2 05 1906 14.12258 1906 5
## 3 06 1906 18.07000 1906 6
## 4 07 1906 21.21290 1906 7
## 5 08 1906 19.69032 1906 8
## 6 09 1906 17.87333 1906 9
plot(MonthlyTMINMean$TMIN, ty='l')
Months = c("January", "February", "March", "April","May", "June", "July", "August", "September", "October","November", "December")
par(mfrow = c(4, 3), mar = c(5, 4, 3, 2) + 0.1)
TMAXresult <- NA
for (i in 1:12)
plot(TMAX ~ Year.num, data = MonthlyTMAXMean[MonthlyTMAXMean$Month.num == i, ], ty = "l", las = 1, xlim = c(1906, 2014), main = Months[i], ylim = c(5, 35))
Month.lm <- lm(TMAX ~ Year.num, data = MonthlyTMAXMean[MonthlyTMAXMean$Month.num == i, ])
summary(Month.lm)
##
## Call:
## lm(formula = TMAX ~ Year.num, data = MonthlyTMAXMean[MonthlyTMAXMean$Month.num ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7267 -1.3327 -0.2605 1.1234 3.9699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06692 10.46142 -0.006 0.995
## Year.num 0.01008 0.00532 1.894 0.061 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.72 on 104 degrees of freedom
## Multiple R-squared: 0.03333, Adjusted R-squared: 0.02404
## F-statistic: 3.586 on 1 and 104 DF, p-value: 0.06105
abline(coef(Month.lm), col = "red")
TMAXresult <- rbind(TMAXresult, cbind(Months[i], round(coef(Month.lm)[2], 4),round(summary(Month.lm)$coefficients[2,4], 4), round(summary(Month.lm)$r.squared,3)))
MonthlyTMINMean = aggregate(TMIN ~ Month + Year, climate_data, mean)
MonthlyTMINMean$Year.num = as.numeric(MonthlyTMINMean$Year)
MonthlyTMINMean$Month.num = as.numeric(MonthlyTMINMean$Month)
head(MonthlyTMINMean)
## Month Year TMIN Year.num Month.num
## 1 04 1906 13.09333 1906 4
## 2 05 1906 14.12258 1906 5
## 3 06 1906 18.07000 1906 6
## 4 07 1906 21.21290 1906 7
## 5 08 1906 19.69032 1906 8
## 6 09 1906 17.87333 1906 9
plot(MonthlyTMINMean$TMIN, ty='l')
Months = c("January", "February", "March", "April","May", "June", "July", "August", "September", "October","November", "December")
par(mfrow = c(4, 3), mar = c(5, 4, 3, 2) + 0.1)
TMINresult <- NA
for (i in 1:12)
plot(TMIN ~ Year.num, data = MonthlyTMINMean[MonthlyTMINMean$Month.num == i, ], ty = "l", las = 1, xlim = c(1906, 2014), main = Months[i])
Month.lm <- lm(TMIN ~ Year.num, data = MonthlyTMINMean[MonthlyTMINMean$Month.num == i, ])
summary(Month.lm)
##
## Call:
## lm(formula = TMIN ~ Year.num, data = MonthlyTMINMean[MonthlyTMINMean$Month.num ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6965 -0.9602 -0.1668 0.9987 2.6743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.343989 8.208805 1.869 0.0644 .
## Year.num -0.002966 0.004175 -0.710 0.4790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 104 degrees of freedom
## Multiple R-squared: 0.00483, Adjusted R-squared: -0.004739
## F-statistic: 0.5048 on 1 and 104 DF, p-value: 0.479
abline(coef(Month.lm), col = "red")
TMINresult <- rbind(TMINresult, cbind(Months[i], round(coef(Month.lm)[2], 4),round(summary(Month.lm)$coefficients[2,4], 4), round(summary(Month.lm)$r.squared,3)))
climate_data$PRCP[climate_data$PRCP==-9999] <- NA
Missing <- aggregate(is.na(climate_data$PRCP),
list(climate_data$Month, climate_data$Year), sum)
Missing$Date = as.numeric(Missing$Group.1) + as.numeric(Missing$Group.2)/12
plot(x ~ Date, data=Missing)
TotalPPT <- aggregate(climate_data$PRCP,
list(climate_data$Month, climate_data$Year), sum, na.rm=T)
names(TotalPPT) = c("Group.1", "Group.2", "ppt")
NonMissing <- Missing[Missing$x < 5, c(1:3)]
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
PPT <- merge(TotalPPT, NonMissing, all.y=TRUE)
PPT$Date <- as.numeric(PPT$Group.1) + as.numeric(PPT$Group.2)/12
head(PPT)
## Group.1 Group.2 ppt x Date
## 1 01 1907 178.4 0 159.9167
## 2 01 1908 127.9 0 160.0000
## 3 01 1909 184.8 0 160.0833
## 4 01 1910 38.9 0 160.1667
## 5 01 1911 170.2 0 160.2500
## 6 01 1912 1.8 0 160.3333
PRCP_mean = mean(PPT$ppt)
plot(ppt~Date, data=PPT)
abline(h=PRCP_mean, col="blue")